Method for isolating the physical location of an element in a physical structure

Abstract

This disclosure generally relates to a computer-implemented method for isolating the physical locations of components within a physical structure. This is consistent with the present disclosure, which involves performing a series of evaluations at evaluation points depending on the type of physical structure and combining the results of the performed evaluations. The solution according to this disclosure can be used in many different technical fields, for example, for identifying erroneous locations within connection components, such as wire harnesses. This disclosure also relates to corresponding computer systems and computer program products.

Description

Technical Field

[0001] This disclosure generally relates to a computer-implemented method for isolating the physical locations of components within a physical structure. This is consistent with the present disclosure, which involves performing a series of evaluations at evaluation points depending on the type of physical structure and combining the results of the performed evaluations. The solution according to this disclosure can be used in many different technical fields, for example, for identifying erroneous locations within connection components, such as wire harnesses. This disclosure also relates to corresponding computer systems and computer program products. Background Technology

[0002] Recent technological advancements have greatly improved the ability to diagnose and troubleshoot problems in complex systems such as vehicle electrical networks. Traditionally, identifying and resolving errors in these systems has been a time-consuming and expertise-intensive process. Relying on human diagnostics, often based on intuition and experience, can lead to inefficiency and inaccuracies.

[0003] The complexity of modern vehicle electrical systems, with their intricate networks of components and connections, exacerbates these challenges. While current methods are advanced, they often fall short in quickly and accurately identifying errors, leading to increased downtime and costs. Furthermore, traditional diagnostic tools and methods may be inadequate for handling the dynamic and interconnected nature of modern vehicle electrical systems, thus necessitating a more systematic and data-driven approach.

[0004] US7096101 provides examples of solutions attempting to overcome the identified problems mentioned above. In US7096101, vehicle diagnostic data is collected and transmitted to an external service center, where personnel may be able to provide additional assistance compared to standard body and auto repair facilities. While US7096101 offers some improvements over allowing workshop operators to handle this themselves, the sheer volume of vehicle analysis data generated during vehicle operation can be so complex that even service center personnel may struggle to understand it. Therefore, service center personnel will not be able to offer workshop operators a “best guess,” typically relying solely on intuition and prior knowledge of similar problems.

[0005] Therefore, improvements are still needed to the methods used to guide users, such as technicians, through complex diagnostic tasks. Ideally, these improvements would enable users to perform these tasks more efficiently, relying on a systematic approach that minimizes guesswork and reduces the time spent locating, for example, errors when performing diagnostic tasks. Summary of the Invention

[0006] Therefore, according to one aspect of this disclosure, a computer implementation method for isolating the physical location of an element in a physical structure is provided, the method comprising the steps of: identifying the type of the element or the physical structure; selecting a first evaluation point within the physical structure based on the identified type of the element or the physical structure; performing a first evaluation at the first evaluation point; selecting a second evaluation point within the physical structure based on the result of the first evaluation, the second evaluation point being different from the first evaluation point; performing a second evaluation at the second evaluation point; and determining the physical location of the element within the physical structure based on the results of the first evaluation and the second evaluation.

[0007] This disclosure is based on the understanding that simplifying the process of determining the physical location of components within an isolated physical structure would offer significant advantages. The scheme proposed in this disclosure achieves this simplified process by implementing the following steps: identifying the type of the component or the physical structure, and based on that identification, iteratively selecting multiple evaluation points within the component / structure to determine the aforementioned specified physical location of the component. Specifically, introducing the identification of the type of the component or the physical structure provides the possibility of fully automating the selection of multiple evaluation points.

[0008] Therefore, the solution according to this disclosure ensures significant progress over the prior art by introducing a systematic, data-driven approach, reducing reliance on often error-prone and inefficient manual diagnostics. By automating the selection of evaluation points and utilizing the results of sequential evaluations, the method improves accuracy and accelerates the isolation process.

[0009] In one embodiment, the identification is performed using at least one of visual analysis, physical or operational characteristic analysis, and contextual data retrieval. These identification methods allow the diagnostic system to adapt to the specific properties of the component and its surrounding structure, thereby providing a flexible and robust fault location approach. Visual analysis may involve techniques such as image recognition or pattern matching, enabling the system to extract identifiable features of the component or structure. For example, a camera or other imaging device may capture physical dimensions, surface textures, or color patterns, which are then processed to determine the type of component. This method is particularly advantageous in environments where visual features can distinguish components, such as differentiating connectors or sub-assemblies in a wiring harness.

[0010] Physical or operational characteristic analysis provides another dimension of adaptability. For example, a system can measure electrical characteristics (e.g., impedance, capacitance, or resistance), thermal characteristics (e.g., thermal features), or acoustic signals (e.g., sound frequencies) associated with the component. This approach is valuable when the functional characteristics of the component are more discriminative than its physical appearance, such as identifying a faulty sensor within an operational network.

[0011] Furthermore, contextual data retrieval allows the system to utilize external information, such as digital twins, CAD models, or historical operational data. By comparing observed features or behaviors with stored data, the system can infer the type of the component. For example, barcodes, RFID tags, or stored manufacturing data can provide accurate identification in structured environments such as warehouses or industrial facilities.

[0012] The proposed multimodal identification framework improves the accuracy and versatility of the system. By combining multiple identification technologies, the system can operate effectively in a wide range of environments and applications. For example, the system can therefore dynamically select the most appropriate identification method based on the environment or available data, ensuring reliable operation even in complex or constrained scenarios. Furthermore, by integrating identification data into the diagnostic process, the system can make more informed decisions regarding the selection of evaluation points, resulting in faster and more accurate fault isolation. In addition, automated identification reduces reliance on operator expertise or human intervention, lowering the risk of errors and improving overall efficiency. Therefore, the combination of vision, physical, and contextual technologies makes the system suitable for applications ranging from diagnosing harness faults to inventory management in storage systems.

[0013] In one embodiment, the physical structure is selected from the group comprising an electrical system and a set of storage compartments. However, it should be readily understood that the solutions according to this disclosure can also be used, or alternatively, in other fields requiring the identification of the physical location of components within a physical structure. For example, such possible implementations can be found in the group comprising locating HVAC systems, piping systems, possible components in data centers, locating critical medical equipment, and for navigating complex utility systems. Of course, other examples are also possible and fall within the scope of this disclosure.

[0014] When applied to electrical systems, the steps of the method can help identify problems within complex circuits or layouts, thereby improving maintenance efficiency. For storage compartments, whose configurations and uses can vary significantly, the method's ability to adjust its evaluation points accordingly can significantly simplify the organization or retrieval process. These examples highlight the inherent flexibility of the method, meeting the diverse needs and complexities inherent in different types of physical structures.

[0015] According to another aspect of this disclosure, a computer-implemented method for isolating erroneous locations within a connection component is provided. The method includes the steps of: identifying the type of the connection component; selecting a first measurement point at the connection component based on the identified type; performing a first measurement at the first measurement point; selecting a second measurement point at the connection component, the second measurement point being different from the first measurement point, based on the result of the first measurement; performing a second measurement at the second measurement point; and isolating erroneous locations within the connection component based on the results of the first and second measurements. This aspect of the disclosure provides similar advantages to those discussed above with respect to the foregoing aspects of this disclosure; however, a specific embodiment is provided herein for isolating erroneous locations within a connection component, such as a wire harness.

[0016] In traditional diagnostic methods, test points within the physical structure, such as wiring harnesses, are typically predetermined based on static information such as wiring diagrams or manufacturer specifications. While this approach provides a baseline for diagnosis, it cannot dynamically adapt to the actual condition of the structure. Significant challenges arise when faults or errors are located in unexpected areas, requiring substantial manual adjustments, additional testing cycles, or reliance on operator intuition. This limitation can lead to inefficiency, increased downtime, and errors in fault localization.

[0017] Furthermore, many existing diagnostic systems lack the ability to iteratively refine their testing focus during the diagnostic process. For example, they may require operators to perform multiple measurements at predefined points without utilizing intermediate results to guide subsequent evaluations. This inability to adapt to the unique characteristics of the structure being inspected limits its accuracy and often leads to unnecessary or redundant assessments.

[0018] The method disclosed herein addresses these challenges by introducing an adaptive diagnostic framework. By dynamically selecting evaluation points based on the results of previous assessments, the method ensures that each subsequent measurement is relevant to and related to the evolving diagnostic process. The iterative approach defined herein minimizes unnecessary evaluations and significantly reduces the time and effort required to locate physical locations within isolated structures.

[0019] Furthermore, the combination of results from multiple assessments enables a higher degree of accuracy in fault localization. This stepwise narrowing down of potential fault areas is particularly advantageous in complex systems such as wiring harnesses, where faults can occur in unpredictable locations. By systematically integrating intermediate results into the selection process of subsequent assessment points, the method achieves diagnostic accuracy exceeding that of static, predefined testing methods.

[0020] According to this disclosure, the process begins with identifying the type of connection component, a desirable step for targeted and efficient fault isolation. This identification, according to this disclosure, is used for the selection of multiple measurement points to ensure optimal selection based on the specific characteristics and complexity of the connection component in question.

[0021] The subsequent selection and execution of measurements at these strategic points ensures the effectiveness of the scheme according to this disclosure in providing relatively high reliability for error isolation. By performing a series of measurements, each based on the result of the previous measurement, the scheme systematically narrows down the potential location of errors. This approach offers significant advantages compared to more conventional methods. Once the connection component is cleared, error location isolation is achieved through careful analysis and synthesis of the data obtained from the measurements. The scheme according to this disclosure not only simplifies the diagnostic process but also improves the accuracy of error location within the connection component. This accuracy is particularly important in systems where the connection component plays a critical role, ensuring repair and maintenance with the highest precision and efficiency.

[0022] In one embodiment of this disclosure, isolation of fault locations is further based on the type of the connection component. Including the type of the connection component as a determining factor in the isolation process improves the accuracy of the method. The method recognizes the diversity of connection components, each with its unique configuration and potential fault regions. By integrating the type of the connection component into the decision-making process for isolating faults, the scheme of this disclosure becomes more adaptable and better responsive to the specific characteristics of the components in question.

[0023] By implementing this embodiment, the diagnostic process can be enhanced by more closely aligning the selection of multiple measurement points and the interpretation of measurement results with the inherent characteristics of the connecting components. This approach ensures that error localization is not only systemic but also deeply influenced by the subtle differences in the specific components being inspected, thereby achieving more accurate and efficient diagnostics.

[0024] Preferably, the step of identifying the type of the connection component includes the following steps: acquiring an image of the connection component; applying an image processing scheme to the acquired image to extract identifiable features of the connection component; and determining the type of the connection component based on the extracted identifiable features.

[0025] As shown above, the acquired images of the connection components are fed into a sophisticated image processing scheme, with a focus on extracting identifiable features from the images. These features can range from variations in physical size and color patterns to more complex details such as connector type or wire arrangement, which are then used to determine the specific type of the connection component.

[0026] The effectiveness of this embodiment is related to its ability to convert visual information into actionable data, thereby adding a layer of detail that may be difficult to obtain in conventional diagnostic methods. By extracting and analyzing these features, a comprehensive understanding of the connected components can be provided, which helps to accurately isolate errors with high precision. This precision is particularly advantageous in complex systems where even minor errors can lead to significant operational challenges.

[0027] In the context of this disclosure, the image of the connection component can be acquired using one or more different sensor systems. Examples of such sensor systems include, for instance, image capture devices (e.g., cameras), LiDAR, radar, laser scanners, thermal sensors, and sensors for time-domain reflectometry. Of course, other current and future sensor systems are also possible and fall within the scope of this disclosure. Naturally, more than one sensor can be combined with an object capture device, such as an image capture device and LiDAR.

[0028] Preferably, the image processing scheme employed according to an embodiment of this disclosure includes a machine learning component that is pre-trained on various connection component types. This training enables the machine learning component to efficiently and accurately identify various connection components based on visual characteristics.

[0029] In this embodiment, the application of a machine learning-based image processing scheme significantly enhances the overall effectiveness of the error isolation method according to this disclosure. By pre-training on various types of connection components, the machine learning component can quickly identify component types, thereby simplifying the error isolation process. This pre-training means that the scheme does not need to be trained individually for each computer system on which it is implemented, but rather benefits from a pre-developed, general, and comprehensive training method.

[0030] In some embodiments, the machine learning component can be executed as a supervised learning process. In this case, it allows operator or user intervention, enabling corrections to the recognition process based on operator feedback. This supervised aspect can be particularly useful in the initial implementation phase of an image processing scheme. Conversely, implementing the machine learning component as an unsupervised process allows for fully autonomous recognition and decision-making, making the scheme highly efficient and reducing the need for human intervention. A hybrid approach combining supervised and unsupervised learning can also be employed, providing flexibility and adaptability at different stages of the machine learning scheme's implementation. This machine learning approach, whether supervised, unsupervised, or a combination of both, represents a significant advancement over traditional image processing methods. By leveraging sophisticated algorithms, including neural networks and other machine learning techniques, the method provides a high level of accuracy and adaptability in identifying connected component types, which is crucial for accurate and efficient error isolation.

[0031] In a further embodiment of this disclosure, the method for isolating faults within a connection component, such as a wire harness, integrates time-domain reflectometry (TDR) in at least one measurement. TDR is a well-established technique for identifying faults in wires by sending a signal along a conductor and observing the reflected signal.

[0032] Incorporating TDR measurements into this method is particularly advantageous for identifying faults within complex interconnected assemblies. When measurements are performed at selected points within the assembly, TDR can provide valuable insights into conductor conditions, such as breaks or defects, by analyzing the characteristics of the reflected signal. The reflected signal is affected by changes in conductor impedance, which can indicate the presence and location of a fault. Within this method, applying TDR at a first measurement point provides an initial assessment of the wiring harness. Based on the results of this TDR measurement, including details such as the distance from the measurement point to the fault, the method subsequently guides the selection of subsequent measurement points. This systematic approach allows for a more focused and efficient fault isolation process.

[0033] The use of a TDR is particularly beneficial in systems with long cable bundles or complex wiring because it can identify faults that may be difficult to locate through visual inspection or other traditional methods. By leveraging the precise fault location capabilities of a TDR, this approach improves the accuracy and efficiency of the diagnostic process, thereby ensuring more reliable maintenance and repair of connected components.

[0034] However, it should be understood that other alternative measurement techniques are possible and fall within the scope of this disclosure. Such alternative measurement techniques may include, for example, voltage drop measurements, which can detect resistance changes indicating faults, and insulation resistance tests, which can be used to identify insulation degradation or breakage. Furthermore, capacitance measurements can be used to detect changes in the electrical characteristics of the wiring harness, indicating problems such as moisture intrusion or insulation degradation.

[0035] Other techniques may include the use of electromagnetic field analysis, which is beneficial for detecting discontinuities in the conductive path of wires, or the application of acoustic emission testing, in which acoustic waves generated by defects or stress in the wire bundle are analyzed. These methods, along with others such as fiber optic testing for wire bundles incorporating optical fibers, or thermal imaging for visualizing and quantifying temperature differences caused by faults, provide a comprehensive range of diagnostic tools. Each of these alternative measurement techniques has unique advantages and can be selected based on the specific requirements of the connection assembly being inspected. Integrating multiple measurement methods within the method ensures a universal and comprehensive approach for fault isolation in various types of connection assemblies, thereby improving the applicability and effectiveness of the method in a wide range of scenarios.

[0036] In another embodiment of this disclosure, the method for isolating faults within a connection assembly is further improved by incorporating the identification of electrical components connected to the assembly. This addition brings an extra dimension to the diagnostic process, improving its accuracy and relevance. Identifying the connected electrical components is a key step in informing the selection of multiple measurement points. Knowing which electrical components are connected to the assembly allows for a more targeted approach when selecting multiple measurement points, as different electrical components can affect the connection assembly in various ways. This may include considering the electrical characteristics of the electrical components, such as their resistance, capacitance, or inductance, which may influence the location where potential faults are most likely to occur.

[0037] For example, if a high-power component is connected to the assembly, the method may prioritize measurement points that are more prone to thermally induced damage or wear. Alternatively, if sensitive signal processing components are involved, the method may focus on points where signal integrity issues are more likely to occur.

[0038] By considering the specific electrical components connected to the connection assembly, the method becomes more adaptable and better suited to the actual operating environment of the component. This results in a more efficient and accurate diagnostic process because the measurement points are selected based on a comprehensive understanding of the actual operation of the component. Therefore, including this step improves the applicability of the method to complex systems where multiple components with different electrical characteristics interact with the connection assembly. It ensures that the fault isolation process is not only systemic but also highly influenced by the specific conditions and requirements of the system being inspected.

[0039] In one embodiment, the electrical components form part of a mobile platform, and the selection of at least one of the first and second measurement points depends on the type of the mobile platform. This embodiment recognizes the importance of the type of mobile platform in influencing the characteristics and potential failure profile of the connected components. By integrating the mobile platform type into the decision-making process for measurement point selection, the method adapts its strategy to the specific operational and environmental conditions encountered by the platform.

[0040] For example, connectivity components in commercial vehicles may be subjected to harsh usage and varying environmental conditions, potentially requiring different diagnostic focuses compared to connectivity components in more controlled environments, such as those in ships or aircraft. Therefore, the method can be tailored accordingly to its measurement point selection, based on the type of mobile platform, targeting areas more prone to wear, environmental stress, or specific usage patterns. This customized approach leads to more relevant and effective diagnostics because it considers the unique stresses and demands imposed on connectivity components by the mobile platform. It ensures that the error isolation process is not only systematic but also incorporates contextual information, thereby improving the accuracy and reliability of the diagnostics.

[0041] In a further embodiment of this disclosure, the method addresses the isolation of specific types of errors within connectivity components. These errors include any form of electrical fault, such as, but not limited to, short circuits within the connectivity component, disconnections within the connectivity component, and corrosion in connectors included in the connectivity component. Each of these errors presents unique challenges and can significantly impact the functionality and reliability of the connectivity component.

[0042] For example, a short circuit can cause power loss or damage to connected components, and precise location is required to prevent potential system failures. The systematic measurement and analysis methods described herein are particularly adept at identifying the location of such faults, even in complex wiring harness configurations.

[0043] Similarly, resolving disconnections within connection components is crucial for maintaining the integrity of electrical systems. The method, through strategically selected point-focused measurements, can detect discontinuities or breaks in the connection path, thereby facilitating timely repairs and preventing further complications.

[0044] Corrosion in connectors is another common problem, especially in environments with moisture or chemical exposure, which can degrade connectivity and signal quality. The method described is significantly advantageous due to its ability to pinpoint the exact location of such corrosion through targeted measurements, allowing for the restoration of optimal connectivity and preventing the spread of corrosion-related damage.

[0045] By specifically addressing these common yet critical errors, the methods disclosed herein provide a comprehensive solution for maintaining and repairing connectivity components in a variety of applications. This targeted approach ensures that the most common and impactful error types are efficiently and accurately isolated, thereby improving the overall reliability and performance of the connectivity components.

[0046] In one possible embodiment according to this disclosure, error isolation within the connection component is further enhanced by incorporating a modeling step that utilizes previously collected data. This modeling involves comparing the results of a first and second measurement with existing data on connection components of the same or corresponding types. The integration of this comparative modeling step represents a significant advancement in the diagnostic process. By referencing previously collected data, the method leverages historical insights and patterns, which can provide a more comprehensive informational basis for isolating error locations. This historical data may include measurements from similar connection components under various conditions, thus providing a rich database that can be crucial for accurately identifying the location of errors.

[0047] For example, information that shows a trend of certain errors occurring at specific points in a particular type of wiring harness in a particular vehicle model can be very valuable. The method uses this data to refine its analysis of current measurements, making the error isolation process more precise and efficient. This approach is particularly beneficial when dealing with complex or recurring problems, where patterns from past instances can provide crucial clues for current diagnosis. Furthermore, the method is able to leverage a wide range of historical data, meaning it can adapt to a diverse range of connection components and error types. This adaptability ensures the method remains effective in different scenarios and continuously enhances its diagnostic capabilities as more data becomes available. Additionally, by incorporating modeling with previously collected data, the method not only becomes more robust in its error isolation capabilities but also evolves with each application, continuously improving its accuracy and reliability for future diagnosis.

[0048] Preferably, the modeling includes applying machine learning processes to selected portions of the different datasets contained in the first vehicle diagnostic data set. In this case, utilizing machine learning processes allows for more detailed and dynamic analysis of the collected data. By applying advanced algorithms and computational techniques, the machine learning processes can identify patterns and correlations in the data that may not be readily apparent using conventional analysis methods. This capability is particularly beneficial when dealing with complex connected components where error symptoms may be subtle or influenced by multiple factors.

[0049] The machine learning process can be adapted to process large datasets and extract meaningful insights, which are crucial for accurately isolating fault locations. For example, it can identify subtle changes in measurement data that indicate a specific type of fault, or it can track gradual changes over time pointing to emerging problems. Furthermore, the adaptability of the machine learning process means that the method can continuously improve its diagnostic accuracy. As more data is collected and analyzed over time, the machine learning model can be refined and tuned, thereby improving its effectiveness in isolating faults within various types of connected components.

[0050] According to another aspect of this disclosure, a computer system suitable for isolating the physical locations of components within a physical structure is provided. The computer system includes a control unit configured to: identify the type of the component or the physical structure; select a first evaluation point within the physical structure based on the identified type of component or physical structure; perform a first evaluation at the first evaluation point; select a second evaluation point within the physical structure, different from the first evaluation point, based on the result of the first evaluation; perform a second evaluation at the second evaluation point; and determine the physical location of the component within the physical structure based on the results of the first and second evaluations. This aspect of the disclosure also provides similar advantages to those discussed above with respect to the foregoing aspects of this disclosure.

[0051] In a preferred embodiment of this disclosure, the computer system is further adapted to form an image to be provided at an output interface, wherein the image is formed by utilizing indications to enhance the representation of the connection components. This augmented reality (AR) scheme can be effectively used to guide a user to identify and perform evaluations at different evaluation points within the physical structure. The AR feedback can be provided in real time, thereby providing dynamic guidance as the user advances the physical location of the elements within the isolated physical structure.

[0052] This AR-based guidance can visually highlight these evaluation points in the user's field of vision and overlay instructions or markers onto them. For example, when using an electronic user device, such as a mobile phone, the user can view the physical structure through the device's camera, while the AR system can overlay necessary information within the user interface that defines the output interface, such as on the display screen, thereby guiding the user to the precise location for evaluation.

[0053] In a more immersive setup, the AR system can be integrated into a head-mounted device equipped with embedded cameras and display elements, worn by the user. This setup frees the user's hands, allowing for easier interaction with the physical structure while receiving continuous visual feedback. The control unit of the computer system can be embedded within the head-mounted device or operate remotely, such as in a cloud-based server setup, providing the computing power required for the AR system.

[0054] AR visual content can be customized based on data that defines the physical structure, the elements to be isolated, and the specific evaluations to be performed at each evaluation point. The advantage of this approach is that it provides not only location guidance but also instruction on how to correctly perform each evaluation, which is particularly beneficial for users less familiar with the technical details of the evaluation process.

[0055] Furthermore, when using multiple cameras, the computer system can provide an enhanced 3D view of the physical structure, thereby improving the accuracy and ease of following AR guidance. Implementing such an AR-based system in diagnostic methods enhances the user experience, making the process more intuitive, efficient, and easy to use, especially for complex physical structures or components.

[0056] According to another aspect of this disclosure, a computer system suitable for isolating erroneous locations within a connectivity component is provided. The computer system includes a control unit configured to: identify the type of the connectivity component; select a first measurement point at the connectivity component based on the identified type of connectivity component; perform a first measurement at the first measurement point; select a second measurement point at the connectivity component, different from the first measurement point, based on the result of the first measurement; perform a second measurement at the second measurement point; and isolate the erroneous location within the connectivity component based on the results of the first and second measurements. Similarly, this aspect of the disclosure also provides similar advantages to those discussed above with respect to the foregoing aspects of this disclosure.

[0057] For example, the control system can be used in the automotive industry, general manufacturing and / or assembly processes.

[0058] According to an additional aspect of this disclosure, a computer program product is provided comprising a non-transitory computer-readable medium storing computer program means for isolating the physical locations of elements within a physical structure. The computer program product includes: code for identifying a type of the element or the physical structure; code for selecting a first evaluation point within the physical structure based on the identified type of element or physical structure; code for performing a first evaluation at the first evaluation point; code for selecting a second evaluation point within the physical structure based on the result of the first evaluation, the second evaluation point being different from the first evaluation point; code for performing a second evaluation at the second evaluation point; and code for determining the physical location of the element within the physical structure based on the results of the first and second evaluations. This aspect of the disclosure also provides similar advantages to those discussed above with respect to the foregoing aspects of this disclosure.

[0059] Software executed by the processing unit to operate according to this disclosure may be stored on a computer-readable medium, which is any type of storage device, including removable non-volatile random access memory, hard disk drive, floppy disk, CD-ROM, DVD-ROM, USB memory, SD memory card, solid-state drive, other non-volatile flash memory-based storage media, or similar computer-readable media known in the art.

[0060] Further features and advantages of this disclosure will become apparent as the appended claims and the following description are examined. Those skilled in the art will recognize that different features of this disclosure can be combined to create embodiments other than those described below, without departing from the scope of this disclosure. Attached Figure Description

[0061] Various aspects of this disclosure, including its particular features and advantages, will be readily understood from the following detailed description and accompanying drawings, in which: Figure 1 A computer system according to a presently preferred embodiment of the present disclosure is conceptually illustrated; Figure 2A and Figure 2B The example illustrates two possible implementations of this computer system for isolating the physical locations of different types of components within different types of physical structures; and Figure 3 This is a flowchart illustrating the steps of performing a method according to a currently preferred embodiment of the present disclosure. Detailed Implementation

[0062] The present disclosure will now be described more fully below with reference to the accompanying drawings, which illustrate presently preferred embodiments of the disclosure. However, the present disclosure may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided for thoroughness and completeness and to fully communicate the scope of the disclosure to those skilled in the art. The same reference numerals refer to the same elements throughout the drawings. The following examples illustrate the disclosure and are not intended to limit it.

[0063] Now turn to the attached diagram, especially Figure 1 The illustration conceptually depicts a computer system 100 suitable for isolating the physical locations of components within a physical structure. The computer system 100 includes at least one computing device in the form of an exemplary control unit 102, which is arranged as a component of a server (not in...). Figure 1 (This is clearly shown in the text).

[0064] The control unit 102 is further arranged to communicate with the object capture device 104, the evaluation device 106, and the output interface 108 for presenting information to a user, for example, using a network connection. In some embodiments of this disclosure, the object capture device 104, the evaluation device 106, and the output interface 108 may be combined into a single mobile unit operated by a user. It should be noted that at least some elements of the processing functions provided by the control unit may be integrated into such a mobile unit.

[0065] exist Figure 1In this embodiment, an object capture device 104 (e.g., a video camera) is shown embedded in an output interface 108, both of which together provide an augmented reality (AR) head-mounted device. The AR head-mounted device preferably includes image and audio generation means for providing information to a user / operator wearing the head-mounted device.

[0066] For reference, the control unit may be, for example, a general-purpose processor, a graphics processing unit, a dedicated processor, a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, a field-programmable gate array (FPGA), etc. The processor may be or include any number of hardware components for performing data, signal, and / or image processing or for executing computer code stored in memory. It may also be implemented using a system-on-a-chip (SoC) and is applicable within the scope of this specification. The memory may be one or more means for storing data and / or computer code to perform or facilitate the various methods described herein. The memory may include volatile or non-volatile memory. The memory may include database components, object code components, script components, or any other type of information structure for supporting the various activities described herein. According to exemplary embodiments, any distributed or local storage device may be used with the systems and methods of this specification. According to exemplary embodiments, the memory may be communicatively connected to the processor (e.g., via circuitry or any other wired, wireless, or network connection) and includes computer code for performing one or more processes described herein.

[0067] In some embodiments, the evaluation device 106 may include, for example, a signal generator and a receiver adapted to provide and receive signals; in an exemplary but not in any way limiting embodiment, it is adapted to perform time-domain reflectometry (TDR). Specifically, in the context of TDR measurement, the signal generator is configured to transmit a signal along a connection component, such as a wire harness, and the receiver is designed to capture reflected signals. TDR technology involves analyzing these reflections to determine the location of a fault within the connection component, such as discontinuities or impedance variations. The accuracy and effectiveness of TDR in isolating faults, particularly in complex wire harnesses, makes it a valuable tool in the diagnostic process.

[0068] In addition to TDR, the evaluation device 106 can also be adapted to a range of other diagnostic techniques. These diagnostic techniques may include, but are not limited to, impedance spectroscopy, voltage drop testing, or continuity testing, each providing unique insights into the health and integrity of connected components. The versatility of the evaluation device 106 lies in its ability to be configured or equipped with a variety of sensors and modules, making it suitable for a wide range of diagnostic scenarios.

[0069] Furthermore, integrating the evaluation device 106 with the control unit and object capture device 104 allows for a cohesive and interactive diagnostic environment. This integration facilitates seamless flow of data and instructions between components, thereby enhancing the user's ability to perform accurate and efficient diagnostics.

[0070] In some embodiments, the evaluation device 106 may also include advanced features such as automatic signal adjustment, real-time data processing, and adaptive measurement protocols. These features contribute to the adaptability and accuracy of the method, ensuring that the device remains effective with different types and complexities of connectivity components.

[0071] By encompassing specific technologies such as TDR and a range of other diagnostic capabilities, Figure 1 The evaluation device 106 serves as the cornerstone of the computer system's functionality. It embodies the computer system 100's emphasis on providing comprehensive, accurate, and user-friendly diagnostic solutions.

[0072] It should be emphasized that other types of evaluation devices 106 can also be used in conjunction with the computer system 100. Such other types of evaluation devices 106 can be selected, for example, as one or a combination of measuring devices and geolocation devices, thereby enabling a comprehensive approach to diagnose and locate components within a variety of physical structures.

[0073] Measuring devices, such as the aforementioned TDR system, provide accuracy in identifying specific faults within connection components, such as wiring harnesses. However, in cases where the physical structure extends beyond the electrical system, other devices can be used in conjunction with the computer system 100. For example, measurement-type evaluation devices 106 may be selected from the group including 3D scanning devices, ultrasonic testing devices, thermal imaging cameras, magnetic field sensors, endoscope cameras, acoustic emission sensors, near-field communication (NFC) and radio frequency identification (RFID) readers.

[0074] 3D scanning equipment plays a crucial role in mapping and visualizing the physical layout of complex structures, particularly storage compartments. This technology creates detailed 3D models, providing a comprehensive view that helps identify optimal evaluation points. Ultrasonic testing equipment is essential for non-destructive testing in a wide range of structures. Especially when maintaining physical integrity is critical, this equipment can detect internal defects or anomalies, thus precisely guiding the selection of evaluation points. Thermal imaging cameras provide the ability to detect thermal characteristics within a structure. This is particularly advantageous for identifying overheated electrical components in electrical systems, helping to isolate potential fault areas.

[0075] Magnetic field sensors in electrical systems help detect magnetic fields generated by electric current. Their use can aid in locating areas within electrical systems for focused evaluation. Endoscopic cameras provide the ability to inspect hard-to-reach or visually obstructed areas within structures. Their application is particularly valuable in inspecting complex wiring conduits or hidden compartments within storage systems. Acoustic emission sensors detect sound waves emitted by structural faults, such as cracks. They are highly effective in both electrical systems and physical storage spaces, identifying areas where structural integrity may be compromised.

[0076] Furthermore, Near Field Communication (NFC) readers are well-suited for scenarios where components within a structure are tagged with NFC chips. They can quickly and accurately identify and locate tagged items, thereby simplifying the process of isolating components within the structure.

[0077] Each of these types of measurement-based evaluation devices 106 enhances the versatility and effectiveness of the computer system 100. They can be used individually or in combination, depending on the specific requirements of the structure being analyzed. This adaptability ensures that the system can handle a wide range of diagnostic scenarios, from complex electrical systems to large storage facilities.

[0078] As described above, the evaluation device 106 may also, or alternatively, include, for example, a geolocation device selected from the group comprising NFC-based systems, BLE beacons, Wi-Fi-based indoor positioning systems, and smart shelves. NFC-based systems can quickly locate specific tagged items within a storage environment. BLE beacons offer similar functionality, providing short-range tracking, which is particularly useful in confined spaces. For larger areas, Wi-Fi-based indoor positioning systems utilize existing network infrastructure to locate items, while UWB technology provides unparalleled accuracy in dense and complex environments.

[0079] Smart shelving units with integrated sensors represent an Internet of Things (IoT) approach that transforms storage areas into intelligent systems capable of real-time monitoring and management. These shelving units can automatically track inventory, providing valuable insights into the contents of the storage area.

[0080] Furthermore, AI-driven image recognition-enhanced computer vision systems can identify and locate items based on visual characteristics. This technology is particularly useful for untagged items, or where NFC or BLE tagging is not practical.

[0081] By combining these different technologies, the evaluation device 106 can be adapted to a wide range of diagnostic and location scenarios. For example, a combination of TDR and NFC systems can be used to diagnose faults within a vehicle's electrical system while simultaneously managing spare parts inventory in a repair shop. Similarly, smart shelves equipped with UWB technology can provide both inventory management and precise item location within a warehouse.

[0082] Turn now Figure 2A It illustrates one possible implementation of the scheme according to this disclosure, for example, using a combination of Figure 1 The computer system described herein.

[0083] exist Figure 2A In, and further reference Figure 3 This provides a physical structure, in this case, in the form of a vehicle 200 including a wiring harness 202. Combined with... Figure 2A The presented embodiment involves isolating or locating errors within wiring harness 202. This is achieved by first identifying the type of physical structure S1, which is defined here as, for example, vehicle 200, wiring harness 202, or possibly electrical connector 204 of wiring harness 202. This identification can be performed, for example, using the described AR headset 203, which includes the object capture device 104 and output interface 108, here arranged at the user's location, specifically in the form of an automotive technician.

[0084] As described above, this recognition can be achieved, for example, using an object capture device 104 at an AR headset 203. As previously mentioned, the object capture device 104 can capture images of, for example, a vehicle 200, a wiring harness 202, or possibly an electrical connector 204 of the wiring harness 202, and provide the captured images to, for example, a control unit 102. The control unit 102 can then be equipped with, for example, a machine learning component (not explicitly shown), which has been pre-trained on a large number of different vehicles, wiring harnesses, electrical connectors, etc., thereby enabling it to quickly identify the type of the physical structure.

[0085] In one embodiment, the identification of the component type is performed based on non-visual data obtained from the physical structure. For example, the type of the component can be determined by analyzing electrical or mechanical characteristics, such as impedance, capacitance, or resistance values ​​measured at specific points within the structure. This method is particularly useful in scenarios where the physical characteristics of the component affect the overall function of the structure, such as identifying the type of electrical components or subsystems within a wiring harness.

[0086] In another embodiment, the identification may involve querying data from an external source associated with the physical structure, such as a database or digital representation of the structure (e.g., a digital twin or CAD model). The system can match observed attributes of the structure, such as geometric dimensions, connection patterns, or operating parameters, with stored data to determine the type of the component. For example, in a manufacturing setting, the type of a mechanical component can be identified using a barcode, QR code, or RFID tag attached to the component or its immediate surroundings.

[0087] Furthermore, the identification of the component type can be based on environmental or operational context. For example, in connection components, the type of the component can be inferred from the operational behavior of the connected components. By monitoring voltage, current, or signal patterns at specific points, the system can distinguish between different types of components, such as signal processing devices, power distribution nodes, or sensors. This method enables robust identification without the need for direct imaging or visual analysis.

[0088] Once the type of the physical structure has been identified, a first evaluation point 206, S2, is selected, wherein the selection is based on the identified physical structure. Figure 2A In this context, the first evaluation point is an electrical connection point designated for measurement, such as one of the evaluation / measurement devices 106 described above.

[0089] At this point, the evaluation / measuring device 106 performs a first evaluation, S3, which in turn produces some form of data that can be evaluated. At this point, the error within the harness 202 may not yet have been isolated. Therefore, to get closer to the error, another (second) evaluation point 208 is selected, S4. This second evaluation point is then selected based on the result of the first evaluation and is also selected differently from the first evaluation point, typically at another location within the harness 202.

[0090] A second measurement and subsequent evaluation are performed at the second evaluation point 208, S5. In some embodiments, the evaluation performed at the second evaluation point 208 may be the same as the evaluation performed at the first evaluation point. However, the scheme according to this disclosure allows a combination of evaluation techniques / equipment to be performed at different evaluation points 206, 208. Therefore, the scheme according to this disclosure allows for mixed evaluations, for example, within harness 202, wherein the computer system 100, via, for example, an AR headset 110, can guide the user to different evaluation points 206, 208 and instruct the user on which type of device to use to perform the evaluation.

[0091] Once both the first and second assessments have been performed, the likelihood of identifying the physical location of the error is greatly increased. Specifically, according to this disclosure, this is achieved by determining, S6, the results of the first and second assessments, and a combination of the first 206 and second 208 assessment points.

[0092] In one embodiment, the results of the first and second evaluations can, for example, be provided to another machine learning component included in the control unit 102, wherein this machine learning component has also been pre-trained for different errors and measurement results that may be expected in different types of physical structures, for example... Figure 2A An example wire harness 202.

[0093] Once the erroneous location within the wiring harness 202 has been determined, it can, for example, be displayed within the AR headset 203.

[0094] It should be understood that in some embodiments, more than two separate measurements / evaluations may need to be performed, such as three, four, five, or even more, to determine, for example, the location of an error. However, in the context of this disclosure, it has been determined that at least two measurements / evaluations are necessary to guide the user in the most appropriate manner and with a sufficient level of reliability.

[0095] exist Figure 2B In this disclosure, examples are illustrated in various contexts, wherein the physical structure is a warehouse 260 having a set of shelves 250. The objective here is to isolate the location of components within this storage environment, specifically, to isolate the location of electrical relays 240. This embodiment demonstrates the versatility of the computer system 100 in adapting to various types of physical structures and components.

[0096] A user equipped with a mobile phone 230 initiates the process by identifying, S1, the type of the physical structure and components. In this scenario, the mobile phone 230, operating as an object capturing device 104, captures images of the warehouse 260 and the shelf 250. These images are analyzed using machine learning components integrated within the mobile phone 230 or connected to the control unit 102 to identify specific characteristics of the warehouse 260 and the shelf 250, as well as the electrical relay 240 to be located.

[0097] Once the physical structure and the type of the components have been identified, the system proceeds to select, S2, a first evaluation point within warehouse 260. This selection is based on information from the identified warehouse characteristics and the potential location of the relay 240. The first evaluation point could be a specific shelf or area of ​​the warehouse where the relay might be located.

[0098] Then, the user performs S3, a first evaluation, at that point using a mobile phone 230, which may be enhanced with additional sensor accessories, to search for the electrical relay 240. This evaluation may involve scanning an NFC tag, using image recognition technology, or employing other sensor-based methods to detect the presence of the relay. Figure 2B In the middle, mobile phone 230 is used to scan QR codes 232 and 234 placed throughout the warehouse 260 and / or on shelves 250.

[0099] Based on the results of the first assessment, a second assessment point is selected, S4. This point is determined to be different from the first assessment point, thereby guiding the user to another potential location of the relay within warehouse 260. This selection is driven by intelligent analysis of the first assessment results and considers various factors such as relay type, shelving organization, and warehouse layout. At the second assessment point, another round of assessment is performed using mobile phone 230, S5. This method allows for a dynamic approach, where different types of assessments can be applied at each point based on the evolving needs of the search process.

[0100] Finally, based on the combined results of the first and second assessments, the physical location of the electrical relay 240 is determined, S6. This determination is facilitated by a machine learning component that has been trained for various scenarios and component types commonly found in warehouse settings. Through this intelligent processing, the system effectively guides the user to the precise location of the electrical relay 240, demonstrating the adaptability and efficiency of the method in storage-centric environments such as warehouse 260.

[0101] It should be noted that the control functions of this disclosure can be implemented using existing computer processors, or by a dedicated computer processor incorporated for a suitable system and for this or other purpose, or by a hardwired system. Embodiments falling within the scope of this disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available medium accessible by a general-purpose computer, a special-purpose computer, or another machine having a processor. For example, such machine-readable media may include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, solid-state drives or other non-volatile flash memory-based storage devices, or any other medium that can be used to carry or store desired program code in the form of machine-executable instructions or data structures and is accessible by a general-purpose computer, a special-purpose computer, or another machine having a processor. When information is transmitted or provided to a machine via a network or another communication connection (hardwired, wireless, or a combination of hardwired and wireless), the machine appropriately considers that connection as a machine-readable medium. Therefore, any such connection is appropriately referred to as a machine-readable medium. The above combinations are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing machine to perform a function or a set of functions.

[0102] Although the accompanying drawings may show the order of steps, the order of steps may differ from the depicted order. Furthermore, two or more steps may be performed simultaneously or partially simultaneously. This variation will depend on the chosen software and hardware system and the designer's choices. All such variations fall within the scope of this disclosure. Similarly, software implementation can be accomplished using standard programming techniques with rule-based logic and other logic to implement various connection steps, processing steps, comparison steps, and decision steps. Moreover, although this disclosure has been described with reference to specific exemplary embodiments, many different changes, modifications, etc., will become apparent to those skilled in the art.

[0103] Furthermore, those skilled in the art, through studying the accompanying drawings, this disclosure, and the appended claims, can understand and implement variations of the disclosed embodiments when practicing the claimed disclosure. Additionally, in the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality.

Claims

1. A computer-based method for isolating the physical locations of components within a physical structure, characterized in that, The method includes the following steps: - Identify the type of the component or the physical structure; - Select the first evaluation point within the identified physical structure based on the type of the component or physical structure; - Perform the first assessment at the first assessment point; - Select a second evaluation point within the physical structure based on the result of the first evaluation, wherein the second evaluation point is different from the first evaluation point; - Perform a second assessment at the second assessment point; and - Determine the physical location of the element within the physical structure based on the results of the first and second evaluations.

2. The method according to claim 1, characterized in that, The identification is performed using at least one of visual analysis, physical or operational characteristic analysis, and contextual data retrieval.

3. The method according to any one of claims 1 and 2, characterized in that, The physical structure is selected from a group that includes an electrical system and a set of storage compartments.

4. A computer-implemented method for isolating erroneous locations within a connection component, characterized in that, The method includes the following steps: - Identify the type of the connection component; - Select a first measurement point at the identified connection component based on the type of the connection component; - Perform the first measurement at the first measurement point; - Select a second measurement point at the connection component based on the result of the first measurement, wherein the second measurement point is different from the first measurement point; - Perform a second measurement at the second measurement point; and - Isolate the erroneous location within the connection component based on the results of the first and second measurements.

5. The method according to claim 4, characterized in that, The identification is performed using at least one of visual analysis, physical or operational characteristic analysis, and contextual data retrieval.

6. The method according to any one of claims 4 and 5, characterized in that, The isolation of the fault location is further based on the type of the connection component.

7. The method according to any one of claims 4 to 6, characterized in that, The steps for identifying the type of the connection component include the following: - Obtain an image of the connection component; - Apply image processing techniques to the acquired images to extract identifiable features of the connecting components; and - The type of the connection component is determined based on the extracted identifiable features.

8. The method according to claim 7, characterized in that, The image processing scheme includes machine learning components pre-trained on various types of connection components.

9. The method according to any one of claims 4 to 8, characterized in that, At least one of the first and second measurements involves performing a time-domain reflectometry (TDR).

10. The method according to any one of claims 4 to 9, characterized in that, It also includes the following steps: Identify the electrical components connected to the connection assembly; The selection of at least one of the first and second measurement points depends on the identified electrical component.

11. The method according to claim 10, characterized in that, The electrical components form part of the mobile platform, and the selection of at least one of the first and second measurement points depends on the type of the mobile platform.

12. The method according to claim 10, characterized in that, The mobile platform is selected from the group consisting of at least one of vehicles, ships, and aircraft.

13. The method according to any one of claims 4 to 12, characterized in that, The error is at least one of the following: a short circuit within the connection assembly, a disconnection within the connection assembly, and corrosion in the connector included in the connection assembly.

14. The method according to any one of claims 4 to 13, characterized in that, The step of isolating the error location includes: modeling the results of the first and second measurements using previously collected corresponding data for the same type of connection component or a corresponding type of connection component.

15. The method according to any one of claims 4 to 14, characterized in that, The modeling involves applying machine learning processes to selected portions of different datasets contained in the first vehicle diagnostic data set.

16. A computer system suitable for isolating the physical locations of components within a physical structure, characterized in that, The computer system includes a control unit, which is configured to: - Identify the type of the component or the physical structure; - Select the first evaluation point within the identified physical structure based on the type of the component or physical structure; - Perform the first assessment at the first assessment point; - Select a second evaluation point within the physical structure based on the result of the first evaluation, wherein the second evaluation point is different from the first evaluation point; - Perform a second assessment at the second assessment point; as well as - Determine the physical location of the element within the physical structure based on the results of the first and second evaluations.

17. A computer system suitable for isolating fault locations within a connection component, characterized in that, The computer system includes a control unit, which is configured to: - Identify the type of the connection component; - Select a first measurement point at the identified connection component based on the type of the connection component; - Perform the first measurement at the first measurement point; - Select a second measurement point at the connection component based on the result of the first measurement, wherein the second measurement point is different from the first measurement point; - Perform a second measurement at the second measurement point; as well as - Isolate the erroneous location within the connection component based on the results of the first and second measurements.

18. An electronic user equipment comprising the computer system of claim 17.

19. A computer program product, characterized in that, The device includes a non-transitory computer-readable medium on which computer program means for isolating the physical locations of elements within a physical structure is stored, the computer program product comprising: - A code used to identify the type of the component or the physical structure; - Code for selecting the first evaluation point within the physical structure based on the type of the identified element or physical structure; - Code used to perform the first evaluation at the first evaluation point; - Code for selecting a second evaluation point within the physical structure based on the result of the first evaluation, wherein the second evaluation point is different from the first evaluation point; - Code for performing a second evaluation at the second evaluation point; and - Code for determining the physical location of the element within the physical structure based on the results of the first and second evaluations.

Images